# chiphuyen/dmls-book

Summaries and resources for Designing Machine Learning Systems book (Chip Huyen, O'Reilly 2022)

Repository: https://github.com/chiphuyen/dmls-book
Canonical: https://ross.abutalabs.com/products/dmls-book
Homepage: https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969
License Family: other
Last push: 2026-06-09T13:28:30+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 86, release rhythm 35, longevity 100
- inputs: {"age_days": 1559, "days_push": 85, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 5237, forks 1064 (observed 2026-08-28T04:09:13.492008+00:00)

## What it is
The official companion repository for the book 'Designing Machine Learning Systems' by Chip Huyen (O'Reilly 2022), containing chapter summaries and curated resources. It supports a holistic approach to designing reliable, scalable, and maintainable production ML systems rather than serving as a code tutorial.

## Use cases
- learn how to design production-ready machine learning systems
- find summaries of ML systems design concepts
- prepare for MLOps or ML engineer interviews
- understand ML system design decisions like data engineering and deployment
- find resources for learning MLOps
- study how to make ML models reliable and scalable in production

## When to choose
- you want structured learning material on ML systems design and MLOps
- you are reading the book and want chapter summaries and extra resources
- you are an ML engineer or data scientist moving models to production

## When to avoid
- you need runnable code or hands-on tutorials
- you want a software tool or library to include in a project
- you need a license-permitted codebase (the repo has no license)

## Facets
- artifact type: learning-resource
- maturity: stable
- function: machine-learning, developer-tools, documentation
- domain: machine-learning, tutorials, data-science
- platform: cross-platform
- tags: book, ml-systems-design, mlops, chip-huyen, oreilly, summaries, education

## Member repositories
- chiphuyen/dmls-book (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:09:13.492008+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:59:37.825508+00:00, confidence not recorded.
  - readme: https://github.com/chiphuyen/dmls-book (fetched 2026-08-28T04:09:13.492008+00:00, sha 5dd9bc5b7ac0)
  - homepage: https://www.amazon.com/Designing-Machine-Learning-Systems-Production-Ready/dp/1098107969 (fetched 2026-08-29T08:55:12.972279+00:00, sha 3001aa0bb18b)
  - site_page: https://www.amazon.com/gp/new-releases (fetched 2026-08-29T08:55:12.981895+00:00, sha 1d52d088625d)
  - site_page: https://www.amazon.com/amz-books/new-releases (fetched 2026-08-29T08:55:12.984119+00:00, sha e5b57401b829)
  - site_page: https://www.amazon.com/kindle-dbs/comics-store/new-releases?_encoding=UTF8 (fetched 2026-08-29T08:55:12.986264+00:00, sha 7809a3f9aa5d)
- Data as of 2026-08-30T08:39:29.467469+00:00.